where \(y_{t}\)  denote the \(t\) -th token of \(y\) , and \(y_{<t}\)  denotes the sequence of tokens preceding \(y_{t}\) . The latent variable \(z_m\)  and \(z_c\)  are concatenated with the hidden states at all steps. Note that our model is also compatible with Transformer-based decoders. But recent empirical work has shown that Transformer-based decoders require meticulous designs, such as meshed attention [1]} or high-order attention [2]}, and greatly increase the time complexity when optimized with reinforcement learning (RL).
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